Bing Open Sources Their Vector Search Algorithm To Make Finding Results Fast

Bing announced they have made their Space Partition Tree And Graph (SPTAG) vector search algorithm open source to help others who want to make it possible to search through billions of pieces of information in milliseconds.

Bing wrote:

Microsoft has made one of the most advanced AI tools it uses to better meet people’s evolving search needs available to anyone as an open source project on GitHub. On Wednesday, it also released user example techniques and an accompanying video for those tools via Microsoft’s AI lab.

The algorithm, called Space Partition Tree And Graph (SPTAG), allows users to take advantage of the intelligence from deep learning models to search through billions of pieces of information, called vectors, in milliseconds. That, in turn, means they can more quickly deliver more relevant results to users.

Vector search makes it easier to search by concept rather than keyword. For example, if a user types in “How tall is the tower in Paris?” Bing can return a natural language result telling the user the Eiffel Tower is 1,063 feet, even though the word “Eiffel” never appeared in the search query and the word “tall” never appears in the result.

Microsoft uses vector search for its own Bing search engine, and the technology is helping Bing better understand the intent behind billions of web searches and find the most relevant result among billions of web pages.

Here is the video explaining it:

Frédéric Dubut from Bing was excited to hear this news and posted about it on Twitter:

My favorite quote from the article: "A stack of 150 billion business cards would stretch from here to the moon. Within a blink of an eye, @Bing’s search using #SPTAG can find 10 different business cards one after another within that stack of cards."